Hands on Exercise 03b: Programming Animated Statistical Graphics with R

Overview

When telling a visually-driven data story, animated graphics will tend to attract the interest of the audience and make a deeper impression than static graphics.

In this exercise, we will create animated data visualisations by using the gganimate and plotly r packages.At the same time, we will also learn how to (i) reshape data by using the tidyr package, and (ii) process, wrangle and transform data by using dplyr package.

Basic concepts of animation

When creating animations, the plot does not actually move. Instead, many individual plots are built and then stitched together as movie frames, just like an old-school flip book or cartoon. Each frame is a different plot when conveying motion, which is built using some relevant subset of the aggregate data. The subset drives the flow of the animation when stitched back together.

Terminology

We first introduce some of the key concepts and terminology related to this type of visualization

• Frame: In an animated line graph, each frame represents a different point in time or a different category. When the frame changes, the data points on the graph are updated to reflect the new data.

• Animation Attributes: settings that control how the animation behaves. For example, you can specify the duration of each frame, the easing function used to transition between frames, and whether to start the animation from the current frame or from the beginning.

Tip

Before making animated graphs, one should ask oneself: Does it make sense to go through the effort? If you are conducting an exploratory data analysis, an animated graphic may not be worth the time investment. However, if you are giving a presentation, a few well-placed animated graphics can help an audience connect with your topic remarkably better than static counterparts.

Loading Data

We will be using the following R packages:

• plotly, R library for plotting interactive statistical graphs.
• gganimate, a ggplot extension for creating animated statistical graphs.
• gifski converts video frames to GIF animations using pngquant’s fancy features for efficient cross-frame palettes and temporal dithering. It produces animated GIFs that use thousands of colors per frame.
• gapminder: An excerpt of the data available at Gapminder.org. We just want to use its country_colors scheme.
• tidyverse, a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs.

pacman::p_load(plotly, gganimate, gifski, gapminder, tidyverse, readxl)

col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
                      sheet="Data") %>%
  mutate_at(col, as.factor) %>%
  mutate(Year = as.integer(Year))

In the above, mutate_at() of dplyr package is used to convert all character data types into factors. A factor is a data type in R used to represent categorical variables. These store data as integer values with corresponding ‘levels’, making them memory-efficient.

Instead of the above using mutate_at(), we can use across to achieve the same output with the basic mutate()

col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
                      sheet="Data") %>%
  mutate(across(col, as.factor)) %>%
  mutate(Year = as.integer(Year))

Animated Data Visualisation: gganimate Methods

gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time.

• transition_*() defines how the data should be spread out and how it relates to itself across time.
• view_*() defines how the positional scales should change along the animation.
• shadow_*() defines how data from other points in time should be presented in the given point in time.
• enter_*()/exit_*() defines how new data should appear and how old data should disappear during the course of the animation.
• ease_aes() defines how different aesthetics should be eased during transitions.

Building a static population bubble plot

We first create a static bubble plot for reference

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') 

Building the animated bubble plot

• transition_time() of gganimate is used to create transition through distinct states in time (i.e. Year).
• ease_aes() is used to control easing of aesthetics. The default is linear. Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back, and bounce.

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7,
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') +
  transition_time(Year) +       
  ease_aes('linear')

Animated Data Visualisation: plotly

In Plotly R package, both ggplotly() and plot_ly() support key frame animations through the frame argument/aesthetic. They also support an ids argument/aesthetic to ensure smooth transitions between objects with the same id (which helps facilitate object constancy).

Building an animated bubble plot: ggplotly() method

The plot

The code

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young')

ggplotly(gg)

Things to learn from the code chunk above

• Appropriate ggplot2 functions are used to create a static bubble plot. The output is then saved as an R object called gg.
• ggplotly() is then used to convert the R graphic object into an animated svg object.

Notice that although show.legend = FALSE argument was used, the legend still appears on the plot. To overcome this problem, theme(legend.position='none') should be used as shown in the plot and code chunk below.

The plot

The code

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young') + 
  theme(legend.position='none')

ggplotly(gg)

Building an animated bubble plot: plot_ly() method

The plot

The code

bp <- globalPop %>%
  plot_ly(x = ~Old, 
          y = ~Young, 
          size = ~Population, 
          color = ~Continent,
          sizes = c(2, 12),
          frame = ~Year, 
          text = ~Country, 
          hoverinfo = "text",
          type = 'scatter',
          mode = 'markers'
          ) %>%
  layout(showlegend = FALSE)
bp

References

• Getting Started
• Building an animation step-by-step with gganimate